Evaluation of RPs MPs Data Set
Arend Lis, Bernett Judith, Manz Quirin
2023-01-08
1 Original Data (not normalized on CD42b)
1.1 Read Data
sce <- readRDS("data/sce_original_RPs_MPs.rds")
de_res_b <- readRDS("data/de_results_baseline_original.rds")
de_res_a <- readRDS("data/de_results_stimulated_original.rds")1.2 Marker Distributions
1.2.1 State Markers
features <- "state"
plotPbExprsMod(sce,
k = "all",
features = features,
assay = "exprs",
fun = "median",
color_by = "subgroup2",
facet_by = "antigen",
shape_by = "activation")
CATALYST::plotExprs(sce,
color_by = "subgroup2",
features = features,
assay = "exprs")
1.2.2 Type Markers
features <- "type"
plotPbExprsMod(sce,
k = "all",
features = features,
assay = "exprs",
fun = "median",
color_by = "subgroup2",
facet_by = "antigen",
shape_by = "activation")
CATALYST::plotExprs(sce,
color_by = "subgroup2",
features = features,
assay = "exprs")
1.3 DE Results
1.3.1 Paired t-test
1.3.1.1 Baseline RPs vs. MPs
plotDiffHeatmapCustom(x = sce,
y = de_res_b$t_test,
col_anno = c("subgroup", "patient_id"),
k = "all",
top_n = 20,
fdr = 0.05,
lfc = 1,
all = TRUE,
eff_r = de_res_b$effect_size,
sort_by = "padj",
assay = "exprs",
fun = "median",
normalize = TRUE
)
print_DE_top_table(sce, de_res_b, method = "t_test")1.3.1.2 Stimulated RPs vs. MPs
plotDiffHeatmapCustom(x = sce,
y = de_res_a$t_test,
col_anno = c("subgroup", "patient_id"),
k = "all",
top_n = 20,
fdr = 0.05,
lfc = 1,
all = TRUE,
eff_r = de_res_a$effect_size,
sort_by = "padj",
assay = "exprs",
fun = "median",
normalize = TRUE
)
print_DE_top_table(sce, de_res_a, method = "t_test")1.3.2 CyEMD
1.3.2.1 Baseline RPs vs. MPs
plotDiffHeatmapCustom(x = sce,
y = de_res_b$CyEMD,
top_n = 20,
fdr = 0.05,
col_anno = c("subgroup", "patient_id"),
k = "all",
lfc = 1,
all = TRUE,
eff_r = de_res_b$effect_size,
sort_by = "padj",
assay = "exprs",
fun = "median",
normalize = TRUE
)
print_DE_top_table(sce, de_res_b, method = "CyEMD")1.3.2.2 Stimulated RPs vs. MPs
plotDiffHeatmapCustom(x = sce,
y = de_res_a$CyEMD,
col_anno = c("subgroup", "patient_id"),
k = "all",
top_n = 20,
fdr = 0.05,
lfc = 1,
all = TRUE,
eff_r = de_res_a$effect_size,
sort_by = "padj",
assay = "exprs",
fun = "median",
normalize = TRUE
)
print_DE_top_table(sce, de_res_a, method = "CyEMD")1.4 DE Comparison
1.4.1 Baseline RPs vs. MPs
createVennHeatmap(de_res_b)
1.4.2 Activated RPs vs. MPs
createVennHeatmap(de_res_a)
2 Normalized Data (on CD42b)
2.1 Read Data
sce <- readRDS("data/sce_CD42b_RPs_MPs.rds")
de_res_b <- readRDS("data/de_results_baseline_CD42b.rds")
de_res_a <- readRDS("data/de_results_stimulated_CD42b.rds")2.2 Marker Distributions
2.2.1 State Markers
features <- "state"
plotPbExprsMod(sce,
k = "all",
features = features,
assay = "exprs",
fun = "median",
color_by = "subgroup2",
facet_by = "antigen",
shape_by = "activation")
CATALYST::plotExprs(sce,
color_by = "subgroup2",
features = features,
assay = "exprs")
2.2.2 Type Markers
features <- "type"
plotPbExprsMod(sce,
k = "all",
features = features,
assay = "exprs",
fun = "median",
color_by = "subgroup2",
facet_by = "antigen",
shape_by = "activation")
CATALYST::plotExprs(sce,
color_by = "subgroup2",
features = features,
assay = "exprs")
2.3 DE Results
2.3.1 Paired t-test
2.3.1.1 Baseline RPs vs. MPs
plotDiffHeatmapCustom(x = sce,
y = de_res_b$t_test,
col_anno = c("subgroup", "patient_id"),
k = "all",
top_n = 20,
fdr = 0.05,
lfc = 1,
all = TRUE,
eff_r = de_res_b$effect_size,
sort_by = "padj",
assay = "exprs",
fun = "median",
normalize = TRUE
)
print_DE_top_table(sce, de_res_b, method = "t_test")2.3.1.2 Stimulated RPs vs. MPs
plotDiffHeatmapCustom(x = sce,
y = de_res_a$t_test,
col_anno = c("subgroup", "patient_id"),
k = "all",
top_n = 20,
fdr = 0.05,
lfc = 1,
all = TRUE,
eff_r = de_res_a$effect_size,
sort_by = "padj",
assay = "exprs",
fun = "median",
normalize = TRUE
)
print_DE_top_table(sce, de_res_a, method = "t_test")2.3.2 CyEMD
2.3.2.1 Baseline RPs vs. MPs
plotDiffHeatmapCustom(x = sce,
y = de_res_b$CyEMD,
top_n = 20,
fdr = 0.05,
col_anno = c("subgroup", "patient_id"),
k = "all",
lfc = 1,
all = TRUE,
eff_r = de_res_b$effect_size,
sort_by = "padj",
assay = "exprs",
fun = "median",
normalize = TRUE
)
print_DE_top_table(sce, de_res_b, method = "CyEMD")2.3.2.2 Stimulated RPs vs. MPs
plotDiffHeatmapCustom(x = sce,
y = de_res_a$CyEMD,
col_anno = c("subgroup", "patient_id"),
k = "all",
top_n = 20,
fdr = 0.05,
lfc = 1,
all = TRUE,
eff_r = de_res_a$effect_size,
sort_by = "padj",
assay = "exprs",
fun = "median",
normalize = TRUE
)
print_DE_top_table(sce, de_res_a, method = "CyEMD")2.4 DE Comparison
2.4.1 Baseline RPs vs. MPs
createVennHeatmap(de_res_b)
2.4.2 Activated RPs vs. MPs
createVennHeatmap(de_res_a)